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<h1>kdog</h1><p><span class="helptopic">Difference of Gaussian kernel</span></p><strong>k</strong> = <span style="color:red>kdog</span>(<strong>sigma1</strong>) is a 2-dimensional difference of Gaussian kernel equal
to KGAUSS(<strong>sigma1</strong>) - KGAUSS(SIGMA2), where <strong>sigma1</strong> &amp;gt; SIGMA2.  By default
SIGMA2 = 1.6*<strong>sigma1</strong>.  The kernel is centred within the matrix <strong>k</strong> whose
half-width H = 3xSIGMA and W=2xH+1.

<strong>k</strong> = <span style="color:red>kdog</span>(<strong>sigma1</strong>, <strong>sigma2</strong>) as above but <strong>sigma2</strong> is specified directly.

<strong>k</strong> = <span style="color:red>kdog</span>(<strong>sigma1</strong>, <strong>sigma2</strong>, <strong>H</strong>) as above but the kernel half-width is specified.

<h2>Notes</h2>
<ul>
  <li>This kernel is similar to the Laplacian of Gaussian and is often used
as an efficient approximation.</li>
</ul>
<h2>See also</h2>
<p>
<a href="matlab:doc kgauss">kgauss</a>, <a href="matlab:doc kdgauss">kdgauss</a>, <a href="matlab:doc klog">klog</a>, <a href="matlab:doc iconv">iconv</a></p>
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